{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd24ff19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "from scipy.stats import entropy\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e863a483",
   "metadata": {},
   "source": [
    "## Reading Cached GPT-3 Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bad200f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration potsawee--wiki_bio_gpt3_hallucination-9bba6a0f21f1b815\n",
      "Found cached dataset parquet (/home/pm574/rds/hpc-work/downloads/hf_datasets/potsawee___parquet/potsawee--wiki_bio_gpt3_hallucination-9bba6a0f21f1b815/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "205067fa41574abe8e4c1c113a3448dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# indices in WikiBio of the evaluated examples\n",
    "ids = load_dataset(\"potsawee/wiki_bio_gpt3_hallucination\")['evaluation']['wiki_bio_test_idx']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6cb1c1",
   "metadata": {},
   "source": [
    "For the files below, you could download them at https://drive.google.com/file/d/1t3pJt0o395rdPHK6h6S3s0bxnscQGzK0/view?usp=drive_link "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bb0b331",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPT-3's reponse\n",
    "# response = openai.Completion.create(\n",
    "#     model=\"text-davinci-003\",\n",
    "#     prompt=f\"This is a passage from Wikipedia about {context}:\\n\\n\",\n",
    "#     temperature=temperature, # 0.0 = deterministic\n",
    "#     max_tokens=max_length,\n",
    "#     logprobs=logprobs,\n",
    "# )\n",
    "# type(response.choices[0].text)     => str\n",
    "# type(response.choices[0].logprobs) => openai.openai_object.OpenAIObject\n",
    "\n",
    "responses = {}\n",
    "for idx in ids:\n",
    "    path = f\"/home/pm574/rds/hpc-work/self-consistency/llm_outputs/passage_top_length/gpt3_davinci_003_FEB15_v1_logprob/{idx}.bin\"\n",
    "    with open(path, \"rb\") as f:\n",
    "        response = pickle.load(f)\n",
    "    responses[idx] = response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddca8ce2",
   "metadata": {},
   "source": [
    "## load `wiki_bio_gpt3_hallucination` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "452bb95c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration potsawee--wiki_bio_gpt3_hallucination-9bba6a0f21f1b815\n",
      "Found cached dataset parquet (/home/pm574/rds/hpc-work/downloads/hf_datasets/potsawee___parquet/potsawee--wiki_bio_gpt3_hallucination-9bba6a0f21f1b815/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c239b12efb424c42bdd1d87e5f71db8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_dataset(\"potsawee/wiki_bio_gpt3_hallucination\")\n",
    "dataset = dataset['evaluation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3a5cfce",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_mapping = {\n",
    "    'accurate': 0.0,\n",
    "    'minor_inaccurate': 0.5,\n",
    "    'major_inaccurate': 1.0,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ae11066",
   "metadata": {},
   "outputs": [],
   "source": [
    "human_label_detect_False   = {}\n",
    "human_label_detect_False_h = {}\n",
    "human_label_detect_True    = {}\n",
    "for i_ in range(len(dataset)):\n",
    "    dataset_i = dataset[i_]\n",
    "    idx = dataset_i['wiki_bio_test_idx']\n",
    "    raw_label = np.array([label_mapping[x] for x in dataset_i['annotation']])\n",
    "    human_label_detect_False[idx] = (raw_label > 0.499).astype(np.int32).tolist()\n",
    "    human_label_detect_True[idx]  = (raw_label < 0.499).astype(np.int32).tolist()\n",
    "    average_score = np.mean(raw_label)\n",
    "    if average_score < 0.99:\n",
    "        human_label_detect_False_h[idx] = (raw_label > 0.99).astype(np.int32).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ba31d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(238, 238, 206)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(human_label_detect_False), len(human_label_detect_True), len(human_label_detect_False_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6414d0e",
   "metadata": {},
   "source": [
    "# Experimental Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77dc4e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unroll_pred(scores, indices):\n",
    "    unrolled = []\n",
    "    for idx in indices:\n",
    "        unrolled.extend(scores[idx])\n",
    "    return unrolled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8882add7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_PR_with_human_labels(preds, human_labels, pos_label=1, oneminus_pred=False):\n",
    "    indices = [k for k in human_labels.keys()]\n",
    "    unroll_preds = unroll_pred(preds, indices)\n",
    "    if oneminus_pred:\n",
    "        unroll_preds = [1.0-x for x in unroll_preds]\n",
    "    unroll_labels = unroll_pred(human_labels, indices)\n",
    "    assert len(unroll_preds) == len(unroll_labels)\n",
    "    print(\"len:\", len(unroll_preds))\n",
    "    P, R, thre = precision_recall_curve(unroll_labels, unroll_preds, pos_label=pos_label)\n",
    "    return P, R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed1d5662",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_AUC(P, R):\n",
    "    print(\"AUC: {:.2f}\".format(auc(R, P)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "becd0b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline1_scores = {} # average_logprob\n",
    "baseline2_scores = {} # lowest_logprob\n",
    "baseline3_scores = {} # average_entropy5\n",
    "baseline4_scores = {} # highest_entropy5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "726b30b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 238/238 [00:01<00:00, 206.39it/s]\n"
     ]
    }
   ],
   "source": [
    "for i_ in tqdm(range(len(dataset))):\n",
    "    dataset_i = dataset[i_]\n",
    "    idx = dataset_i['wiki_bio_test_idx']\n",
    "    sentences = dataset_i['gpt3_sentences']\n",
    "    num_sentences = len(sentences)\n",
    "    \n",
    "    # from the cache\n",
    "    passage = responses[idx].choices[0].text\n",
    "    tokens = responses[idx].choices[0].logprobs.tokens\n",
    "    token_logprobs = responses[idx].choices[0].logprobs.token_logprobs\n",
    "    top_logprobs = responses[idx].choices[0].logprobs.top_logprobs\n",
    "    \n",
    "    average_logprob_sent_level = [None for _ in range(num_sentences)]\n",
    "    lowest_logprob_sent_level = [None for _ in range(num_sentences)]\n",
    "    average_entropy5_sent_level = [None for _ in range(num_sentences)]\n",
    "    highest_entropy5_sent_level = [None for _ in range(num_sentences)]\n",
    "        \n",
    "    for sent_i, sentence in enumerate(sentences):\n",
    "        # sentence exist in the passage, so we need to find where it is [i1, i2]\n",
    "        xarr = [i for i in range(len(tokens))]\n",
    "        for i1 in xarr:\n",
    "            mystring = \"\".join(tokens[i1:])\n",
    "            if sentence not in mystring:\n",
    "                break\n",
    "        i1 = i1 - 1\n",
    "        for i2 in xarr[::-1]:\n",
    "            mystring = \"\".join(tokens[i1:i2+1])\n",
    "            if sentence not in mystring:\n",
    "                break\n",
    "        i2 = i2 + 1\n",
    "        mystring = \"\".join(tokens[i1:i2+1])\n",
    "        average_logprob = np.mean(token_logprobs[i1:i2+1])\n",
    "        lowest_logprob  = np.min(token_logprobs[i1:i2+1])\n",
    "        entropy5s = []\n",
    "        for top5_tokens in top_logprobs[i1:i2+1]:\n",
    "            logprob_of_top5_tokens = [x[1] for x in list(top5_tokens.items())]\n",
    "            logprob_of_top5_tokens = np.array(logprob_of_top5_tokens)\n",
    "            prob_of_top5_tokens = np.exp(logprob_of_top5_tokens)\n",
    "            total_prob_of_top5 = prob_of_top5_tokens.sum()\n",
    "            normalized_prob = prob_of_top5_tokens / total_prob_of_top5\n",
    "            # this was designed to be PPL, and intial results showed a better performance level than just entropy\n",
    "            entropy5 = 2**(entropy(normalized_prob, base=2)) \n",
    "            entropy5s.append(entropy5)\n",
    "        average_entropy5 = np.mean(entropy5s)\n",
    "        highest_entropy5 = np.max(entropy5s)\n",
    "\n",
    "        average_logprob_sent_level[sent_i] = average_logprob\n",
    "        lowest_logprob_sent_level[sent_i] = lowest_logprob\n",
    "        average_entropy5_sent_level[sent_i] = average_entropy5\n",
    "        highest_entropy5_sent_level[sent_i] = highest_entropy5\n",
    "\n",
    "    baseline1_scores[idx] = average_logprob_sent_level\n",
    "    baseline2_scores[idx] = average_entropy5_sent_level\n",
    "    baseline3_scores[idx] = lowest_logprob_sent_level\n",
    "    baseline4_scores[idx] = highest_entropy5_sent_level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f205c702",
   "metadata": {},
   "source": [
    "## Experiment1: Detecting False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "935890c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af27f9e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len: 1908\n",
      "len: 1908\n",
      "len: 1908\n",
      "len: 1908\n",
      "-----------------------\n",
      "Baseline1: Avg(logP)\n",
      "AUC: 83.21\n",
      "-----------------------\n",
      "Baseline2: Avg(H)\n",
      "AUC: 80.73\n",
      "-----------------------\n",
      "Baseline3: Max(-logP)\n",
      "AUC: 87.51\n",
      "-----------------------\n",
      "Baseline4: Max(H)\n",
      "AUC: 85.75\n"
     ]
    }
   ],
   "source": [
    "# with human label, Detecting False\n",
    "Pb1, Rb1 = get_PR_with_human_labels(baseline1_scores, human_label_detect_False, pos_label=1, oneminus_pred=True)\n",
    "Pb2, Rb2 = get_PR_with_human_labels(baseline2_scores, human_label_detect_False, pos_label=1)\n",
    "Pb3, Rb3 = get_PR_with_human_labels(baseline3_scores, human_label_detect_False, pos_label=1, oneminus_pred=True)\n",
    "Pb4, Rb4 = get_PR_with_human_labels(baseline4_scores, human_label_detect_False, pos_label=1)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline1: Avg(logP)\")\n",
    "print_AUC(Pb1, Rb1)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline2: Avg(H)\")\n",
    "print_AUC(Pb2, Rb2)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline3: Max(-logP)\")\n",
    "print_AUC(Pb3, Rb3)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline4: Max(H)\")\n",
    "print_AUC(Pb4, Rb4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04173dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7295597484276729"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "for v in human_label_detect_False.values():\n",
    "    arr.extend(v)\n",
    "random_baseline = np.mean(arr)\n",
    "random_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "021d80df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Recall')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with human label, Detecting Non-factual\n",
    "plt.figure(figsize=(5.5, 4.5))\n",
    "plt.hlines(y=random_baseline, xmin=0, xmax=1.0, color='grey', linestyles='dotted', label='Random') \n",
    "plt.plot(Rb1, Pb1, '-', label='Avg(logP)')\n",
    "plt.plot(Rb2, Pb2, '-', label='Avg(H)')\n",
    "plt.plot(Rb3, Pb3, '-', label='Max(-logP)')\n",
    "plt.plot(Rb4, Pb4, '-', label='Max(H)')\n",
    "plt.legend()\n",
    "plt.ylim(0.7,1.02)\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.xlabel(\"Recall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263cab97",
   "metadata": {},
   "source": [
    "## Experiment2: Detect False* (harder/less-trivial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da8388a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len: 1632\n",
      "len: 1632\n",
      "len: 1632\n",
      "len: 1632\n",
      "-----------------------\n",
      "Baseline1: Avg(logP)\n",
      "AUC: 38.89\n",
      "-----------------------\n",
      "Baseline2: Avg(H)\n",
      "AUC: 37.09\n",
      "-----------------------\n",
      "Baseline3: Max(-logP)\n",
      "AUC: 35.88\n",
      "-----------------------\n",
      "Baseline4: Max(H)\n",
      "AUC: 32.43\n"
     ]
    }
   ],
   "source": [
    "# with human label, Detecting False\n",
    "Pb1, Rb1 = get_PR_with_human_labels(baseline1_scores, human_label_detect_False_h, pos_label=1, oneminus_pred=True)\n",
    "Pb2, Rb2 = get_PR_with_human_labels(baseline2_scores, human_label_detect_False_h, pos_label=1)\n",
    "Pb3, Rb3 = get_PR_with_human_labels(baseline3_scores, human_label_detect_False_h, pos_label=1, oneminus_pred=True)\n",
    "Pb4, Rb4 = get_PR_with_human_labels(baseline4_scores, human_label_detect_False_h, pos_label=1)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline1: Avg(logP)\")\n",
    "print_AUC(Pb1, Rb1)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline2: Avg(H)\")\n",
    "print_AUC(Pb2, Rb2)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline3: Max(-logP)\")\n",
    "print_AUC(Pb3, Rb3)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline4: Max(H)\")\n",
    "print_AUC(Pb4, Rb4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63ae5415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2971813725490196"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "for v in human_label_detect_False_h.values():\n",
    "    arr.extend(v)\n",
    "random_baseline = np.mean(arr)\n",
    "random_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e6e01d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Recall')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with human label, Detecting Non-factual*\n",
    "plt.figure(figsize=(5.5, 4.5))\n",
    "plt.hlines(y=random_baseline, xmin=0, xmax=1.0, color='grey', linestyles='dotted', label='Random') \n",
    "plt.plot(Rb1, Pb1, '-', label='Avg(logP)')\n",
    "plt.plot(Rb2, Pb2, '-', label='Avg(H)')\n",
    "plt.plot(Rb3, Pb3, '-', label='Max(-logP)')\n",
    "plt.plot(Rb4, Pb4, '-', label='Max(H)')\n",
    "plt.legend()\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.xlabel(\"Recall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d660a81",
   "metadata": {},
   "source": [
    "## Experiment3: Detecting True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fd5a85b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len: 1908\n",
      "len: 1908\n",
      "len: 1908\n",
      "len: 1908\n",
      "-----------------------\n",
      "Baseline1: Avg(logP)\n",
      "AUC: 53.97\n",
      "-----------------------\n",
      "Baseline2: Avg(H)\n",
      "AUC: 52.07\n",
      "-----------------------\n",
      "Baseline3: Max(-logP)\n",
      "AUC: 50.46\n",
      "-----------------------\n",
      "Baseline4: Max(H)\n",
      "AUC: 50.27\n"
     ]
    }
   ],
   "source": [
    "# with human label, Detecting True\n",
    "Pb1, Rb1 = get_PR_with_human_labels(baseline1_scores, human_label_detect_True, pos_label=1, oneminus_pred=False)\n",
    "Pb2, Rb2 = get_PR_with_human_labels(baseline2_scores, human_label_detect_True, pos_label=1, oneminus_pred=True)\n",
    "Pb3, Rb3 = get_PR_with_human_labels(baseline3_scores, human_label_detect_True, pos_label=1, oneminus_pred=False)\n",
    "Pb4, Rb4 = get_PR_with_human_labels(baseline4_scores, human_label_detect_True, pos_label=1, oneminus_pred=True)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline1: Avg(logP)\")\n",
    "print_AUC(Pb1, Rb1)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline2: Avg(H)\")\n",
    "print_AUC(Pb2, Rb2)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline3: Max(-logP)\")\n",
    "print_AUC(Pb3, Rb3)\n",
    "print(\"-----------------------\")\n",
    "print(\"Baseline4: Max(H)\")\n",
    "print_AUC(Pb4, Rb4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5de7c978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27044025157232704"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "for v in human_label_detect_True.values():\n",
    "    arr.extend(v)\n",
    "random_baseline = np.mean(arr)\n",
    "random_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "709d02e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Recall')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with human label, Detecting Non-factual*\n",
    "plt.figure(figsize=(5.5, 4.5))\n",
    "plt.hlines(y=random_baseline, xmin=0, xmax=1.0, color='grey', linestyles='dotted', label='Random') \n",
    "plt.plot(Rb1, Pb1, '-', label='Avg(logP)')\n",
    "plt.plot(Rb2, Pb2, '-', label='Avg(H)')\n",
    "plt.plot(Rb3, Pb3, '-', label='Max(-logP)')\n",
    "plt.plot(Rb4, Pb4, '-', label='Max(H)')\n",
    "plt.legend()\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.xlabel(\"Recall\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
